Improved integration of single-cell transcriptome and surface protein expression by LinQ-View

Lei Li, Haley L. Dugan, Christopher T. Stamper, Linda Yu Ling Lan, Nicholas W. Asby, Matthew Knight, Olivia Stovicek, Nai Ying Zheng, Maria Lucia Madariaga, Kumaran Shanmugarajah, Maud O. Jansen, Siriruk Changrob, Henry A. Utset, Carole Henry, Christopher Nelson, Robert P. Jedrzejczak, Daved H. Fremont, Andrzej Joachimiak, Florian Krammer, Jun HuangAly A. Khan, Patrick C. Wilson

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells).

Original languageEnglish
Article number100056
JournalCell Reports Methods
Volume1
Issue number4
DOIs
StatePublished - Aug 23 2021

Keywords

  • CITE-seq
  • computational method
  • gene expression
  • integrated model
  • mRNA
  • multimodal method
  • protein
  • purity metric
  • scRNA-seq

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